Source code for greatx.nn.models.supervised.median_gcn
from typing import List
import torch.nn as nn
from greatx.nn.layers import MedianConv, Sequential, activations
from greatx.utils import wrapper
[docs]class MedianGCN(nn.Module):
r"""Graph Convolution Network (GCN) with
median aggregation (MedianGCN)
from the `"Understanding Structural Vulnerability
in Graph Convolutional Networks"
<https://www.ijcai.org/proceedings/2021/310>`_ paper (IJCAI'21)
Parameters
----------
in_channels : int,
the input dimensions of model
out_channels : int,
the output dimensions of model
hids : List[int], optional
the number of hidden units for each hidden layer, by default [16]
acts : List[str], optional
the activation function for each hidden layer, by default ['relu']
reduce : str
aggregation function, including {'median', 'sample_median'},
where :obj:`median` uses the exact median as the aggregation function,
while :obj:`sample_median` appropriates the median with a fixed set
of sampled nodes. :obj:`sample_median` is much faster and
more scalable than :obj:`median`. By default, :obj:`median` is used.
dropout : float, optional
the dropout ratio of model, by default 0.5
bias : bool, optional
whether to use bias in the layers, by default True
normalize : bool, optional
whether to compute symmetric normalization
coefficients on the fly, by default False
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer,
by default False
Examples
--------
>>> # MedianGCN with one hidden layer
>>> model = MedianGCN(100, 10)
>>> # MedianGCN with two hidden layers
>>> model = MedianGCN(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # MedianGCN with two hidden layers, without first activation
>>> model = MedianGCN(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # MedianGCN with deep architectures, each layer has elu activation
>>> model = MedianGCN(100, 10, hids=[16]*8, acts=['elu'])
>>> # MedianGCN with sample median aggregation
>>> model = MedianGCN(100, 10, reduce='sample_median')
See also
--------
:class:`greatx.nn.layers.MedianConv`
"""
@wrapper
def __init__(self, in_channels: int, out_channels: int,
hids: List[int] = [16], acts: List[str] = ['relu'],
reduce: str = 'median', dropout: float = 0.5,
bn: bool = False, normalize: bool = False, bias: bool = True):
super().__init__()
conv = []
assert len(hids) == len(acts)
for hid, act in zip(hids, acts):
conv.append(
MedianConv(in_channels, hid, bias=bias, normalize=normalize,
reduce=reduce))
if bn:
conv.append(nn.BatchNorm1d(hid))
conv.append(activations.get(act))
conv.append(nn.Dropout(dropout))
in_channels = hid
conv.append(
MedianConv(in_channels, out_channels, bias=bias,
normalize=normalize, reduce=reduce))
self.conv = Sequential(*conv)
[docs] def forward(self, x, edge_index, edge_weight=None):
""""""
return self.conv(x, edge_index, edge_weight)